Topological Analysis Method Research Articles

Network analysis of contractors’ assessment factors

The effect of assessment management and selecting the appropriate contractor is growing interest in research because of ‎the change in preferences of contractors’ behaviour and the growth of competitiveness. The assessment of contractors is a continuous process that demands ongoing ‎monitoring to maintain high quality ranks. In this study, the factors behaviour of contractors' assessment for a construction engineering company and a method of topological analysis are examined to a) aid in the interpretation of existing relationships among all factors and b) find influencing factors of contractors’ assessment. The method begins with the network construction of factors considering some criteria or standards. We then propose a method of network analysis and describe the results of studies undertaken to apply the correlation network-based approach and discuss the implications of the methodology. As a result of this study, all influencing factors are involved in a category. The most influential factor is the specialized training of staff that can improve the contractors' productivity and competitiveness.

Problems of using phenol (hydroxybenzene) and parabens as pharmaceutical stabilizers: analysis using machine learning methods

Background. Phenol and parabens exert bactericidal properties, are relatively low-toxic (in acute toxicity tests) and are used in pharmaceutical, cosmetic and food industries as stabilizers/preservatives for the final product. Despite their widespread use, the long-term toxicological effects of phenol and parabens remain largely unexplored.Objective: to conduct an analysis of the results of basic and clinical studies on chronic toxicity of phenol and parabens.Material and methods. The study included 544 articles found using the query “Preservatives, Pharmaceutical [MeSH Terms] AND Phenol [MeSH Terms]” in the PubMed/MEDLINE biomedical publications database. Methods of topological and metric analysis of big data were applied, developed in the scientific school of Academician of the Russian Academy of Sciences Yu.I. Zhuravlev. Keywords were sorted by empirical Rudakov–Torshin informativeness functionals in the context of combinatorial theory of solvability, followed by combinatorial testing of solvability to find terms with the greatest informativeness.Results. Despite the existence of individual studies on the acute toxicity of phenol and its derivatives (including parabens), the chronic toxicity of phenol and parabens remains poorly understood. This fact is indicated not only by a lack of carefully performed research, but also by the information in safety data sheets supplied by manufacturers of the relevant substances. The associations of phenol and paraben blood levels with certain chronic pathologies in humans have been insufficiently studied. At the same time, the authors of fundamental research, if not “sound the alarm,” then strongly underline the need to conduct large-scale clinical trials on the long-term toxic effects of phenol and parabens. Firstly, this is due to complex estrogen-like effect of phenol and parabens, including (1) effects on estrogen sulfotransferases, (2) direct interactions with estrogen receptors, (3) influence on the expression of steroid receptor genes. Secondly, the available data from fundamental research indicate that phenol/parabens obviously stimulate the the molecular mechanisms of oncogenesis pathophysiology (systematic disturbances in gene expression and corresponding changes in the structure of organ tissues). Thirdly, teratogenic and other toxic effects on the embryo and pregnancy were demonstrated not only in experimental studies (neurotoxicity and teratogenesis in animal models), but also in clinical observations (metabolic disorders in a pregnant woman, including the metabolism of purines and fatty acids beta-oxidation, hyperactivity and/or excess body weight in children, asthma, thyroid dysfunction, etc.).Conclusion. Findings from basic research and selected clinical studies dictate an urgent need to examine the association of phenol/paraben blood levels with chronic pathologies in large-scale clinical trials with cross-sectional and longitudinal design. The lack of indication on toxic effects of parabens and phenols in certain clinical studies may just be an artifact of incorrect data analysis.

Topology Analysis and Structural Optimization of Air Suspension Mechanical-Vibration-Reduction Wheels

This paper designs a kind of air suspension mechanical-vibration-reduction wheel for mining engineering vehicles; the research work on topology analysis and the structural optimization of the inner and outer rims are carried out with this wheel as the research object. Using Workbench finite-element analysis software, taking the results of static analysis and modal analysis of the two as constraints, a variety of structural improvement styles are obtained through a topology analysis method and compared and verified, and a more reasonable improvement result is selected and assembled into a whole wheel for final analysis and verification. The results show that the optimization results of the wheel still meet the design’s load-bearing requirements, and the weight is lighter; the topology analysis results are ideal.

Key node identification for a network topology using hierarchical comprehensive importance coefficients.

Key nodes are similar to important hubs in a network structure, which can directly determine the robustness and stability of the network. By effectively identifying and protecting these critical nodes, the robustness of the network can be improved, making it more resistant to external interference and attacks. There are various topology analysis methods for a given network, but key node identification methods often focus on either local attributes or global attributes. Designing an algorithm that combines both attributes can improve the accuracy of key node identification. In this paper, the constraint coefficient of a weakly connected network is calculated based on the Salton indicator, and a hierarchical tenacity global coefficient is obtained by an improved K-Shell decomposition method. Then, a hierarchical comprehensive key node identification algorithm is proposed which can comprehensively indicate the local and global attributes of the network nodes. Experimental results on real network datasets show that the proposed algorithm outperforms the other classic algorithms in terms of connectivity, average remaining edges, sensitivity and monotonicity.

A dynamic topology analysis method for multi-ship encounters based on multi time-space network trees

This paper innovatively proposes a novel approach of topological analysis in the dynamic time-space domain of ship encounters under the joint action of multi-space network structural elements, based on the GIS spatial information platform and spatial topology analysis theory. The proposed method based on the ship's navigational motion information, encounter dynamic mode, relative time-space position distribution, etc., the method divides and intersects the time-space domain of the ship's encounter dynamics, and establishes an encountering dynamic time-space network tree in which space and attribute elements are collaboratively associated during the navigation. From the perspective of network topology analysis, the high-precision analysis of the spatial coupling characteristics and time-space trends of potential encountered dangers during ships dynamic navigation is achieved. To verify the effectiveness of the method, real encounter scenarios of M/T SANCHI and M/V CF Crystal were used as analysis cases. The results showed that the proposed method can precisely extract spatial relationships and spatio-temporal coupling feature information of potential hazards encountered between ships, with an average error of about 2.54% and an overall error between 1.94% and 3.53%.Simultaneously, the method's time efficiency results are thoroughly studied and compared, and it is discovered that the proposed method has good stability.

Combinatorial and algorithmic complexity of crystal structures

Research subject. Numeral indexes describing the complexity of the system of contacts between structural units in crystal structures. Aim. Development of a complexity index for the system of contacts between periodic structural units based on the indices available for those between structural units in island (molecular) structures. Materials and methods. Structural data were selected from the COD, AMCSD, and CSD crystallographic databases. The system of contacts in the structures was analyzed by the Voronoi–Dirichlet polyhedra (VDP) method in the ToposPro software package. Results. The method of topological analysis of the system of contacts in molecular crystals was adapted to all heterodesmic crystal structures and tested on the structures of compounds of several classes. Target complexity indices were developed. Conclusions. Networks of contacts between periodic structural units are low-dimensional. A generalized structural class for such networks can be derived from the original crystal structure data. The algorithmic complexity of heterodesmic structures is subadditive, in contrast to superadditive combinatorial complexity. For the first time, the number of bearing contacts was calculated between periodic structural units, reflecting the algorithmic complexity of the structure at the appropriate level of structural description.

Topological Deep Learning Model for Thyroid Multi-Class Categorization

This study presents an innovative method for categorizing thyroid disorders using a topological deep learning (TDL) model. The strategy utilizes supervised learning and places particular emphasis on topological data analysis (TDA). The work employs a dataset obtained from the WEKA simulator, consisting of 32 characteristics and 3897 instances. It creates a predictive model using the WEKA 3.8.4 simulator, deviating from conventional machine learning methods by giving priority to topological features to improve prediction and classification. The TDL model includes the steps of data preprocessing, selecting unique features, and using TDL algorithms such as Naive Bayes, Decision Trees, and Artificial Neural Networks. The model utilizes embedding to extract information, investigates the importance of specific dimensions, and applies an algorithm to make accurate predictions. The results demonstrate excellent performance, with accuracy, recall, F1-score, and ROC values of 0.96, 0.97, 0.95, and 0.96, respectively. The multi-class classification study provides additional validation of the model's effectiveness, demonstrating an exceptional accuracy of 97.83% and a minimal error rate of 0.21. This highlights the model's precision and dependability, making it suitable for possible clinical applications. This study highlights the efficacy of using topological characteristics and topological data analysis (TDA) methods in the classification of thyroid conditions into many classes. This approach shows potential for enhancing accuracy and dependability in intricate classification tasks.

K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell–cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis. Traditional PCA, a main workhorse in dimensionality reduction, lacks the ability to capture geometrical structure information embedded in the data, and previous graph Laplacian regularizations are limited by the analysis of only a single scale. We propose a topological Principal Components Analysis (tPCA) method by the combination of persistent Laplacian (PL) technique and L2,1 norm regularization to address multiscale and multiclass heterogeneity issues in data. We further introduce a k-Nearest-Neighbor (kNN) persistent Laplacian technique to improve the robustness of our persistent Laplacian method. The proposed kNN-PL is a new algebraic topology technique which addresses the many limitations of the traditional persistent homology. Rather than inducing filtration via the varying of a distance threshold, we introduced kNN-tPCA, where filtrations are achieved by varying the number of neighbors in a kNN network at each step, and find that this framework has significant implications for hyper-parameter tuning. We validate the efficacy of our proposed tPCA and kNN-tPCA methods on 11 diverse benchmark scRNA-seq datasets, and showcase that our methods outperform other unsupervised PCA enhancements from the literature, as well as popular Uniform Manifold Approximation (UMAP), t-Distributed Stochastic Neighbor Embedding (tSNE), and Projection Non-Negative Matrix Factorization (NMF) by significant margins. For example, tPCA provides up to 628%, 78%, and 149% improvements to UMAP, tSNE, and NMF, respectively on classification in the F1 metric, and kNN-tPCA offers 53%, 63%, and 32% improvements to UMAP, tSNE, and NMF, respectively on clustering in the ARI metric.

Post global financial crisis multiplex financial network structure longevity: Does it predict re-globalization? Empirical investigation for 234 countries

Research background: International capital flows show decreasing trend after each financial crisis as countries become unwilling to invest in risky counterparties. Recent discussions over structural changes within the global financial network suggest that its structure may be still undergoing some changes. Uncovered weaknesses of over-connectedness with unstable un-ally countries during global financial crisis were further highlighted by recent Covid-19 pandemic. However, previous research suggest that important structural changes happened after global financial crisis. Thus, such situation raises a question whether post global financial crisis multiplex network structure is long-term and is it exhibiting re-globalization pattern as suggested by re-globalization megatrend. Purpose of the article: The main objective of the paper is to assess post global financial crisis multiplex financial network structure longevity and to identify whether it exhibits re-globalization pattern. Methods: Multiplex financial network mapping, network topological analysis and structure longevity methods were employed to evaluate network connectedness, topological structural properties of multiplex financial network and its’ structure longevity. 5-layer multiplex financial network was mapped for different types of capital flows for each layer: net direct and portfolio investments in debt, equity and net banking assets for each year in the post global financial crisis period 2009–2020. Each network layer employed bilateral data from up to 234 countries of the world. Next step of research employed a set of network-level measures defining topological features of each network in the period of 2009–2020 in order to capture the trend of structural changes and evidence of re-globalization. The final stage analyzes multiplex financial network structure by layer and country longevity. Findings & value added: Analysis reveals that stock (positions) multiplex financial network aggregate connectedness, i.e., density and value, increased during post- global financial crisis period until Covid-19 pandemic period, thus, suggesting that globalization in terms of stock is continuing. Topological structural properties of multiplex financial network changed as number of strongly and weakly connected countries decreased and more countries became included in network. Longevity analysis of layer and country structure within multiplex financial network reveal that banking and debt investments are increasing since 2018, while equity part is decreasing. Developed countries, i.e., the United Kingdom and Germany, decrease in multiplex financial network structure, while developing, especially from Asia region (i.e., Hong Kong SAR, China and Singapore) increase. Such empirical results support the re-globalization megatrend in terms of investment type and counterparty structure as highlighted by most recent scientific discussion showing that it started early after global financial crisis. Research also uncovers some regional trends of Asia region increasing in investment network structure, while Europe decreasing. As developing countries attract more and more investment and further develop, global cost saving and, thus, returns from global investment into these countries may be decreasing, resulting in lower gain from globalization. Our analysis may facilitate investment strategy decisions by suggesting that even though gains from globalization might be decreasing, instead of localization, countries could follow ally investment strategy to manage the risk of over-dependency from unstable counterparty while preserving globalization wealth.

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein’s crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

Effects of curved vanes on aerodynamic performance and flow structures in highly loaded tandem cascades

Combining tandem cascades and curved vanes can mitigate the separation in highly loaded compressor cascades. Flow loss mechanisms in curved tandem cascades are crucial for fundamental design and optimization. This study investigates the flow losses and vortex structures in curved tandem cascades with different curved angles and an original straight tandem cascade. Numerical simulations are conducted at variable incidence angles. The results indicate no consistent effect of curved angles on the aerodynamic performance of curved tandem cascades. The tandem cascade with a curved angle of 5° effectively reduces losses at all incidence angles, while the tandem cascade with a curved angle of 15° extends the critical incidence angle to 3°. The comparative analyses reveal that curved vanes increase spanwise pressure gradients, causing low-energy fluid near endwalls to move toward the midspan. The weighting coefficient of losses quantitatively describes that curved vanes weaken the trailing edge shedding vortices of both vanes and passage vortex while enhancing the end wall spanwise vortex of the front vane, thus affecting the loss distribution. Finally, this study innovatively applies topological analysis methods to curved tandem cascades, establishing the relationship between topological structures and vortex structures in corner regions of tandem cascades, which provides a valid research approach to reveal the vortex dynamics mechanisms of the loss distribution in tandem cascades influenced by curved vanes.

Persistent Homology Identifies Pathways Associated with Hepatocellular Carcinoma from Peripheral Blood Samples

Topological data analysis (TDA) methods have recently emerged as powerful tools for uncovering intricate patterns and relationships in complex biological data, demonstrating their effectiveness in identifying key genes in breast, lung, and blood cancer. In this study, we applied a TDA technique, specifically persistent homology (PH), to identify key pathways for early detection of hepatocellular carcinoma (HCC). Recognizing the limitations of current strategies for this purpose, we meticulously used PH to analyze RNA sequencing (RNA-seq) data from peripheral blood of both HCC patients and normal controls. This approach enabled us to gain nuanced insights by detecting significant differences between control and disease sample classes. By leveraging topological descriptors crucial for capturing subtle changes between these classes, our study identified 23 noteworthy pathways, including the apelin signaling pathway, the IL-17 signaling pathway, and the p53 signaling pathway. Subsequently, we performed a comparative analysis with a classical enrichment-based pathway analysis method which revealed both shared and unique findings. Notably, while the IL-17 signaling pathway was identified by both methods, the HCC-related apelin signaling and p53 signaling pathways emerged exclusively through our topological approach. In summary, our study underscores the potential of PH to complement traditional pathway analysis approaches, potentially providing additional knowledge for the development of innovative early detection strategies of HCC from blood samples.

Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal–Organic Frameworks: Application to the Adsorptive Separation of C3 Components

The shape and topology of pores have significant impacts on the gas storage properties of nanoporous materials. Metal–organic frameworks (MOFs) are ideal materials with which to tailor to the needs of specific applications, due to properties such as their tunable structure and high specific surface area. It is, therefore, particularly important to develop descriptors that accurately identify the topological features of MOF pores. In this work, a topological data analysis method was used to develop a topological descriptor, based on the pore topology, which was combined with the Extreme Gradient Boosting (XGBoost) algorithm to predict the adsorption performance of MOFs for methane/ethane/propane. The final results show that this descriptor can accurately predict the performance of MOFs, and the introduction of the topological descriptor also significantly improves the accuracy of the model, resulting in an increase of up to 17.55% in the R2 value of the model and a decrease of up to 46.1% in the RMSE, compared to commonly used models that are based on the structural descriptor. The results of this study contribute to a deeper understanding of the relationship between the performance and structure of MOFs and provide useful guidelines and strategies for the design of high-performance separation materials.

Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture.

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.

Modeling and analysis of spatial difference and evolution of agricultural parks based on topological segmentation and cluster analysis

When the traditional evolution model studies the regional distribution of agricultural parks, the relationship between regions is not clear enough, which leads to the lack of generality of regional distribution. In order to solve this problem, this study adopts the methods of topological division and cluster analysis, establishes the model of regional diversity and evolution of farms, and clusters the spatial information of agricultural parks. According to the feature factors, the images are classified, and the image dimension values are reduced. The data space is divided into cellular space by the method of network topology structure division, and the effective coefficient of each cell is calculated, and the spatial structure and characteristics of agricultural parks are extracted to reveal the similarities and differences between different parks. The experimental results show that the evolution model of agricultural parks constructed by topological division and clustering method shows obvious clustering characteristics in space, and the relationships among the factors are good. It is proved that the model can describe the spatial differences and evolution trends of agricultural parks more accurately, so as to provide more targeted suggestions for the planning, management and sustainable development of agricultural parks.

Exploring Contemporary Calligraphic Creation and Aesthetic Characteristics Using Topological Data Analysis Methods

Abstract In the process of digitization in today's society, the organic combination of technological means and traditional arts, such as calligraphy culture, is a topic that China is facing. The topological data analysis method is used in this paper to conduct an in-depth study on the creation and aesthetic characteristics of contemporary calligraphy, taking into account the situation above. In this paper, the topological data analysis method is first utilized to calculate the position of the critical points in the vector field and draw the topological structure of the vector field. The Jacobi matrix is computed, and the numerical solutions of the eigenvalues and eigenvectors in the matrix are used to pinpoint critical points. The TDA algorithm allows for the visualization and modeling of aesthetic features in contemporary calligraphy creation using the data required for the acquired bulk body model. The aesthetic features of calligraphy creation are analyzed through simulation experiments in the end. The results of simulation experiments show that the TDA algorithm completes the convergence before 500 iterations under a 4-fold filter, the nodes are mainly concentrated in the range of [500, 1000], and the convergence speed is the fastest among the five algorithms. Testing the topological data analysis algorithm for the recognition performance of aesthetic features of calligraphy creation, the recognition rate of TDA is as high as 98.30%, which has a good recognition effect.

Research on digital rural landscape garden landscape planning under rural revitalization strategy based on topological data analysis method

Abstract The advancement of rural revitalization strategies underscores the increasing significance of landscape garden construction. This study optimizes scenic road routing within rural garden planning to support these strategies. By refining the topological data analysis method with Particle Swarm Optimization (PSO), we develop a model for rural scenic road routing. The model utilizes selected suitability indexes, organized through factor analysis into four categories: scenic, land, industrial resources, and infrastructure. Applied to a case study (Route Z), the model identifies Route 2 as the most suitable option, scoring 2.933. Our findings offer innovative insights and technical approaches for effectively planning rural scenic roads, contributing to countryside revitalization efforts.

Identification of hub genes and key pathways in arsenic-treated rice (Oryza sativa L.) based on 9 topological analysis methods of CytoHubba.

Arsenic is a toxic metalloid that can cause acute and chronic adverse health problems. Unfortunately, rice, the primary staple food for more than half of the world's population, is generally regarded as a typical arsenic-accumulating crop plant. Evidence indicates that arsenic stress can influence the growth and development of the rice plant, and lead to high concentrations of arsenic in rice grain. But the underlying mechanisms remain unclear. In the present research, the possible molecules and pathways involved in rice roots in response to arsenic stress were explored using bioinformatics methods. Datasets that involving arsenic-treated rice root and the "study type" that was restricted to "Expression profiling by array" were selected and downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the arsenic-treated group and the control group were obtained using the online web tool GEO2R. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to investigate the functions of DEGs. The protein-protein interactions (PPI) network and the molecular complex detection algorithm (MCODE) of DEGs were analyzed using STRING and Cystoscope, respectively. Important nodes and hub genes in the PPI network were predicted and explored using the Cytoscape-cytoHubba plug-in. Two datasets, GSE25206 and GSE71492, were downloaded from Gene Expression Omnibus (GEO) database. Eighty common DEGs from the two datasets, including sixty-three up-regulated and seventeen down-regulated genes, were then selected. After functional enrichment analysis, these common DEGs were enriched mainly in 10 GO items, including glutathione transferase activity, glutathione metabolic process, toxin catabolic process, and 7 KEGG pathways related to metabolism. After PPI network and MCODE analysis, 49 nodes from the DEGs PPI network were identified, filtering two significant modules. Next, the Cytoscape-cytoHubba plug-in was used to predict important nodes and hub genes. Finally, five genes [Os01g0644000, PRDX6 (Os07g0638400), PRX112 (Os07g0677300), ENO1(Os06g0136600), LOGL9 (Os09g0547500)] were verified and could serve as the best candidates associated with rice root in response to arsenic stress. In summary, we elucidated the potential pathways and genes in rice root in response to arsenic stress through a comprehensive bioinformatics analysis.

Fabp5 is a common gene between a high-cholesterol diet and acute pancreatitis.

Hypercholesterolemia has been identified as risk factor for severe acute pancreatitis (AP). We aimed to identify the common differentially expressed genes (DEGs) between a high-cholesterol diet and AP. We retrived gene expression profiles from the GEO database. DEGs were assessed using GEO2R. For AP hub genes, we conducted functional enrichment analysis and protein-protein interaction (PPI) analysis. GeneMANIA and correlation analysis were employed to predict potential DEG mechanisms. Validation was done across various healthy human tissues, pancreatic adenocarcinoma, peripheral blood in AP patients, and Sprague-Dawley rats with AP. The gene "Fabp5" emerged as the sole common DEG shared by a high-cholesterol diet and AP. Using the 12 topological analysis methods in PPI network analysis, Rela, Actb, Cdh1, and Vcl were identified as hub DEGs. GeneMANIA revealed 77.6% physical interactions among Fabp5, TLR4, and Rela, while genetic correlation analysis indicated moderate associations among them. Peripheral blood analysis yielded area under the ROC curve (AUC) values of 0.71, 0.63, 0.74, 0.64, and 0.91 for Fabp5, TLR4, Actb, Cdh1 genes, and artificial neural network (ANN) model respectively, in predicting severe AP. In vivo immunohistochemical analysis demonstrated higher Fabp5 expression in the hyperlipidemia-associated AP group compared to the AP and control groups. Fabp5 emerged as the common DEG connecting a high-cholesterol diet and AP. Rela was highlighted as a crucial hub gene in AP. Genetic interactions were observed among Fabp5, TLR4, and Rela. An ANN model consisting of Fabp5, TLR4, Actb, and Cdh1 was helpful in predicting severe AP.

A novel matrix topology-based analysis method of thermal circuits for reliability optimization of HSER

Hermetically Sealed Electromagnetic Relay (HSER), used in aviation and aerospace, demands high reliability due to its critical applications. Given its complex operating conditions, efficient thermal analysis is essential for optimizing reliability. The commonly used Finite Element Method (FEM) is often time-consuming and may not be efficient or adaptable for complex multi-dimensional system calculations and design processes. This paper introduces an analysis method for thermal networks based on matrix perspective technology, encompassing matrix transformation, backpropagation of the heat path model, temperature rise calculation, solution comparison, and product implementation. Using the similarity theory of heat circuits, a basic thermal unit is established. Based on the fundamental connection between key components, a thermal network for a typical HSER is designed. An experimental system is set up, and the thermal network model's accuracy is confirmed using test data. Employing the topology analysis method, the topology of the thermal network is analyzed under both coil-energized and de-energized states. Potential thermal paths are identified, leading to optimized solutions for the HSER. Utilizing these solutions, the thermal path matrix topology model is backpropagated to the thermal path for temperature rise calculations. When compared to prototype HSER test data, the efficiency and accuracy of this matrix topology-based analysis method are confirmed.